this post was submitted on 22 Dec 2023
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Wouldn't this absolutely hammer the battery though, or at least give the CPU a hard time? My understanding is that offloading the work to a cloud platform means that the processor-intensive inputting, parsing, generating, and outputting operations are done in purpose-built datacentres, and end user devices just receive the prepared answer.
Wouldn't this rinse the battery and increase the overall device temperature for "normal" end users?
Fair warning: I haven't read the two papers outlined in the article.
CPUs can have special hardware accelerators for stuff like this, and you'd be surprised how powerful our little phone CPUs are and how optimized stuff like this can become.
Awesome, thanks for the insight.
I'm showing my age here, but much like we had math coprocessors running beside the 286 and 386 gen CPUs to take on floating point operations; then graphics cards offloaded geometry-based math operations to GPU's - are we looking at AI-style die or chips to specifically work on AI functions?
Excuse my oversimplification, this isn't my field of expertise!
Well, your not too off. Like ASICs are made for mining cryptocurrency. Specialized processing designed for specific computations. This indeed make it's efficiency greater than a general purpose CPU.
Yes!
Apple added (a while back) what they call a “Neural Engine,” which is hardware dedicated to efficient execution of ML workloads.
https://en.m.wikipedia.org/wiki/Apple_A11
They have been refining it ever since. I would not be surprised if they made advancements in both the hardware and software used for local GAI.
And Google did the same with the Tensor Processor Unit in the Pixels.
not a dedicated chip per se, the trend is to build it directly into the SoC (mobile devices) or the dedicated GPU